[REFINE] graphmode code (#12540)

python/llm/example/GPU/GraphMode/convert-model-textgen-to-classfication.py

@@ -25,15 +25,11 @@
 
 dtype=torch.bfloat16
 num_labels = 5
-
 model_name=model_path
-
 save_directory = model_name + "-classification"
 
-# Initialize the tokenizer
-# Need padding from the left and padding to 1024
+# Initialize the tokenizer 
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-# tokenizer.padding_side = "left"
 tokenizer.pad_token = tokenizer.eos_token
 tokenizer.save_pretrained(save_directory)
 

python/llm/example/GPU/GraphMode/gpt2-graph-mode-benchmark.py

@@ -17,6 +17,7 @@
 import torch
 import time
 import argparse
+import contextlib
 from transformers import GPT2ForSequenceClassification, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig, Qwen2ForSequenceClassification
 from torch.profiler import profile, record_function, ProfilerActivity, schedule
 
@@ -36,12 +37,6 @@
 model_path = args.model_path
 print(f"The batch size is: {batch_size}, device is {device}")
 
-
-######################################################################################
-# PyTorch Profiling with IPEX
-# export IPEX_ZE_TRACING=1
-# export ZE_ENABLE_TRACING_LAYER=1
-import contextlib
 def profiler_setup(profiling=False, *args, **kwargs):
     if profiling:
         return torch.profiler.profile(*args, **kwargs)
@@ -55,33 +50,25 @@ def profiler_setup(profiling=False, *args, **kwargs):
     active=1
     )
 
-# also define a handler for outputing results
+# define a handler for outputing results
 def trace_handler(p):
     if(device == 'xpu'):
         print(p.key_averages().table(sort_by="self_xpu_time_total", row_limit=20))
     print(p.key_averages().table(sort_by="cpu_time_total", row_limit=20))
-    # p.export_chrome_trace("./trace_" + str(p.step_num) + ".json")
-#######################################################################################
-
-
 
 dtype = torch.bfloat16 if device == 'cpu' else torch.float16
 num_labels = 5
-
 model_name = model_path
-
 model_name = model_name + "-classification"
 model_name_ov = model_name + "-ov"
 model_name_ov = model_name_ov + "-fp16"
 
 if (engine == 'ipex') :
     import torch
     import intel_extension_for_pytorch as ipex
-    # Need padding from the left and padding to 1024
     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
     tokenizer.padding_side = "left"
     tokenizer.pad_token = tokenizer.eos_token
-
     model = AutoModelForSequenceClassification.from_pretrained(model_name, torch_dtype=dtype,
                                                                pad_token_id=tokenizer.eos_token_id,
                                                                low_cpu_mem_usage=True
@@ -106,122 +93,55 @@ def trace_handler(p):
     tokenizer.pad_token = tokenizer.eos_token
     model = OVModelForSequenceClassification.from_pretrained(model_name_ov, torch_dtype=dtype).to(device)
 
-
-
 # Intel(R) Extension for PyTorch*
 if engine == 'ipex':
     if device == 'cpu':
-        # model = ipex.llm.optimize(model, dtype=dtype, inplace=True, deployment_mode=True)
-        # ############## TorchDynamo ###############
         model = ipex.optimize(model, dtype=torch.bfloat16, weights_prepack=False)
         model = torch.compile(model, backend='ipex')
-        # ##########################################
     else:    # Intel XPU
-        #model = ipex.llm.optimize(model, dtype=dtype, device="xpu", inplace=True)
         model = ipex.optimize(model, dtype=dtype, inplace=True)
-
     model=torch.compile(model, backend="inductor")
     print(model)
-
-    # # #######calulate the total num of parameters########
-    # def model_size(model):
-    #     return sum(t.numel() for t in model.parameters())
-    # print(f"GPT2 size: {model_size(model)/1000**2:.1f}M parameters")
-    # # # #######print model information  ###################
-    # print(model)
-
-    # ########Enable the BetterTransformer  ###################
-    # only Better Transformer only support GPT2, not support Qwen2
-    # model = BetterTransformer.transform(model)
-#elif engine == 'ipex-llm':
-#    model = ipex.optimize(model, dtype=dtype, inplace=True)
-#    model=torch.compile(model) #backend="inductor")
 elif engine == 'ov':
     print("OV inference")
 
 
 prompt = ["this is the first prompt"]
 prompts = prompt * batch_size
-#print(prompts)
-
-# Tokenize the batch of prompts
 inputs = tokenizer(prompts, return_tensors="pt", padding="max_length", max_length=1024, truncation=True)
-# print(inputs)
 
 if engine == 'ipex' or engine == 'ipex-llm':
-    #ipex need move the inputs to device, but OV doesn't need
     inputs.to(device)
-
-    # Initialize an empty list to store elapsed times
     elapsed_times = []
-
-    # Loop for batch processing 10 times and calculate the time for every loop
     with profiler_setup(profiling=enable_profile, activities=[ProfilerActivity.CPU, ProfilerActivity.XPU],
         schedule=my_schedule,
         on_trace_ready=trace_handler,
-        # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log/gpt2'),
         record_shapes=True,
         with_stack=True
         ) as prof:
-
         for i in range(10):
             start_time = time.time()
-
-            # Perform inference
             with torch.inference_mode():
-                # logits = model(**inputs).logits
                 outputs = model(**inputs)
                 logits = outputs.logits
-
-            # Get the predicted class for each input in the batch
             predicted_class_ids = logits.argmax(dim=1).tolist()
-
             end_time = time.time()
             elapsed_time = end_time - start_time
-
-            # Save the elapsed time in the list
             elapsed_times.append(elapsed_time)
-
             if(enable_profile):
                 prof.step()
-
-            # print(outputs)
-            # print(type(outputs))
-            # print("logits.shape is " + str(logits.shape))
-            # print(logits)
-
-            # print(predicted_class_ids)
-
 elif engine == 'ov':
     print("OV inference")
-        # Initialize an empty list to store elapsed times
     elapsed_times = []
-
-    # Loop for batch processing 10 times and calculate the time for every loop
     for i in range(10):
         start_time = time.time()
-
         outputs = model(**inputs)
         logits = outputs.logits
-
-        # Get the predicted class for each input in the batch
         predicted_class_ids = logits.argmax(dim=1).tolist()
-
         end_time = time.time()
         elapsed_time = end_time - start_time
-
-        # Save the elapsed time in the list
         elapsed_times.append(elapsed_time)
 
-        # print(outputs)
-        # print(type(outputs))
-        # print("logits.shape is " + str(logits.shape))
-        # print(logits)
-
-        # print(predictions)
-        #print(predicted_class_ids)
-
-
 # Skip the first two values and calculate the average of the remaining elapsed times
 average_elapsed_time = sum(elapsed_times[2:]) / len(elapsed_times[2:])
 classfication_per_second = batch_size/average_elapsed_time